Life as a Physicist

The Particle Physics Version of the Anecdote December 13, 2010

Anecdotes are wonderful things, used (and misused) all the time. They tell great little stories, can be the seed of a new idea, or bring down an argument. Have something that is always true? Then you need but one anecdote to bring it tumbling to the ground. People fighting the evolution vs. creationism battle know this technique well! Of course, it is often misused too – an anecdote does not a theory make or break!

In experimental particle physics we have our own version of an anecdote: the event display. In the anecdotal sense we use it mostly in the sense that it is the seed of a new idea. Our eyes and brain are better at recognizing a new pattern than any computer algorithm currently known. I’ve often said that gut instinct does play a role in physics – and the event display is one place where we learn our gut instinct!

You are looking at the inner detector of ATLAS – first (from inner to outer) are the highly accurate pixel detectors, then the silicon strip detectors, and finally all the dots are the transition radiation detector (TRT). The hits from a simulated Hidden Valley event are shown. Now, so the average particle physicist most of that display looks very normal, and wouldn’t even raise an eyebrow. Except for two features. Opposite each other, just above and below the horizontal, there are two plumes of particles. While plumes of particles (“jets”) are not uncommon, the fact that they draw to a point a long way – meters – from the center of the detector is. Very uncommon in fact.

Your eye can pick those out right away. Perhaps, if you aren’t a particle physicist, you didn’t realize those were unique, but I bet your eye got them right away, regardless. Now, the problem is to develop a computer algorithm to pick those guys out. It may look trivial – after all something that your eye gets that easily can’t be that hard – but it turns out not to be the case. Especially using full blown tracking to find those guys… tracking that is tuned to find a track that originates from the center of the detector. Just starting at it like this I’m having a few ideas of things we could do to find those tracks.

Say you already have an algorithm, but it fails some 30% of the time. Then you might take 100 interactions that fail, make event displays of all of them, create a slide show, and then just watch them one after the other. If you are lucky you’ll start to see a pattern.

None of this proves anything, unfortunately. Anecdotes aren’t science. But they do lead to ideas that can be tested! Once you have an idea for the algorithm you can write some code – which is not affected by human bias! – and run it on your sample of interactions. Now you can test it, and you measure its performance and see if your idea is going to work. By measuring you’ve turned your anecdote into science.

That is what I mean by the event display can be the germ of an idea. I’ve seen this technique used a number of times in my field. Though not enough! Our event displays are very hard to use and so many of us (myself included) tend to use them as a last resort. This is unfortunate, because when looking for some new sort of pattern recognition algorithm – as in this case – they are incredibly valuable. Another trend I’ve noticed – the old generation seems to resort to these much quicker than the younger ones. <cough>bubble chambers<cough>.

Just like with real anecdotes, we particle physicists misused our event displays all the time. The most public example is we show an event display at a conference and then call it “a typical event.” You should chuckle. Anytime you hear that it is code for “we searched and searched for the absolutely cleanest event we could find that most clearly demonstrates what we want you to think of as normal and that probably will happen less than once every year.” <smile>

I hope no special skills: computer science is not something we teach any of our students/postdocs/faculty formally! Of course, we all have to be decently versed in it.

There are well known algorithms that can be applied for this type of pattern recognition. However there aren’t libraries we can use, normally – usually we have to start from scratch. The reason is the algorithm is often tightly tied to the detector geometry.

How long? Timescale for a project like this would be expected to be months. That would be for a first version. Then over the course of years it would be slowly improved as each successive generation would add to it.

🙂 So, if your audience is a bunch of HEP folks, then the word typical is fine – because most of us know what you mean. If it is a more general audience then just say what it is – “Take this exceptional event which clearly demonstrates the xxx” or similar. In your thesis you could just ignore the issue and say “see fig x for a clear visual demonstration of the effect” or somethign like that.. which I guess is the same. But I agree, avoid the word “typical” in the thesis.